
from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
				import PIL

if is_torch_available():
				import torch


snake_case__					=				logging.get_logger(__name__)







class 				UpperCAmelCase						( _lowerCAmelCase       ):
					a__:			List[Any]      	=					["""pixel_values"""]

					def __init__(      self   :       Optional[int]			,   lowerCAmelCase   :       Optional[int] = True			,   lowerCAmelCase   :       List[Any] = None			,   lowerCAmelCase   :       Optional[int] = PILImageResampling.BILINEAR			,   lowerCAmelCase   :       str = True			,   lowerCAmelCase   :       Tuple = 1 / 255			,   lowerCAmelCase   :       List[str] = True			,   lowerCAmelCase   :       Union[str, Any] = None			,   lowerCAmelCase   :       int = True			,   **lowerCAmelCase   :       Union[str, Any]			,   ):
											super().__init__(**SCREAMING_SNAKE_CASE_       )
											lowercase							:     str									=	size if size is not None else {"""shortest_edge""": 224}
											lowercase							:     Optional[int]									=	get_size_dict(SCREAMING_SNAKE_CASE_			,   default_to_square=SCREAMING_SNAKE_CASE_       )
											lowercase							:     Any									=	crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
											lowercase							:     int									=	get_size_dict(SCREAMING_SNAKE_CASE_			,   param_name='''crop_size'''       )

											lowercase							:     Tuple									=	do_resize
											lowercase							:     Any									=	size
											lowercase							:     int									=	resample
											lowercase							:     Dict									=	do_rescale
											lowercase							:     Tuple									=	rescale_factor
											lowercase							:     Optional[int]									=	do_center_crop
											lowercase							:     List[str]									=	crop_size
											lowercase							:     Tuple									=	do_flip_channel_order

					def 							_lowerCAmelCase						(      self   :       Any			,   lowerCAmelCase   :       Union[str, Any]			,   lowerCAmelCase   :       Any			,   lowerCAmelCase   :       Optional[Any] = PIL.Image.BILINEAR			,   lowerCAmelCase   :       Union[str, Any] = None			,   **lowerCAmelCase   :       Optional[int]			,   ):
											lowercase							:     List[str]									=	get_size_dict(SCREAMING_SNAKE_CASE_			,   default_to_square=SCREAMING_SNAKE_CASE_       )
											if "shortest_edge" not in size:
																	raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''       )
											lowercase							:     int									=	get_resize_output_image_size(SCREAMING_SNAKE_CASE_			,   size=size['''shortest_edge''']			,   default_to_square=SCREAMING_SNAKE_CASE_       )
											return resize(SCREAMING_SNAKE_CASE_			,   size=SCREAMING_SNAKE_CASE_			,   resample=SCREAMING_SNAKE_CASE_			,   data_format=SCREAMING_SNAKE_CASE_			,   **SCREAMING_SNAKE_CASE_       )

					def 							_lowerCAmelCase						(      self   :       List[str]			,   lowerCAmelCase   :       int			,   lowerCAmelCase   :       str			,   lowerCAmelCase   :       List[str] = None			,   **lowerCAmelCase   :       List[str]			,   ):
											lowercase							:     str									=	get_size_dict(SCREAMING_SNAKE_CASE_       )
											if "height" not in size or "width" not in size:
																	raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''       )
											return center_crop(SCREAMING_SNAKE_CASE_			,   size=(size['''height'''], size['''width'''])			,   data_format=SCREAMING_SNAKE_CASE_			,   **SCREAMING_SNAKE_CASE_       )

					def 							_lowerCAmelCase						(      self   :       List[Any]			,   lowerCAmelCase   :       str			,   lowerCAmelCase   :       str			,   lowerCAmelCase   :       int = None			,   **lowerCAmelCase   :       Dict			,   ):
											return rescale(SCREAMING_SNAKE_CASE_			,   scale=SCREAMING_SNAKE_CASE_			,   data_format=SCREAMING_SNAKE_CASE_			,   **SCREAMING_SNAKE_CASE_       )

					def 							_lowerCAmelCase						(      self   :       Tuple			,   lowerCAmelCase   :       Any			,   lowerCAmelCase   :       str = None       ):
											return flip_channel_order(SCREAMING_SNAKE_CASE_			,   data_format=SCREAMING_SNAKE_CASE_       )

					def 							_lowerCAmelCase						(      self   :       Any			,   lowerCAmelCase   :       str			,   lowerCAmelCase   :       Tuple = None			,   lowerCAmelCase   :       Union[str, Any] = None			,   lowerCAmelCase   :       int = None			,   lowerCAmelCase   :       Tuple = None			,   lowerCAmelCase   :       List[Any] = None			,   lowerCAmelCase   :       Optional[Any] = None			,   lowerCAmelCase   :       List[Any] = None			,   lowerCAmelCase   :       Dict = None			,   lowerCAmelCase   :       int = None			,   lowerCAmelCase   :       Any = ChannelDimension.FIRST			,   **lowerCAmelCase   :       Dict			,   ):
											lowercase							:     List[Any]									=	do_resize if do_resize is not None else self.do_resize
											lowercase							:     List[Any]									=	resample if resample is not None else self.resample
											lowercase							:     Tuple									=	do_rescale if do_rescale is not None else self.do_rescale
											lowercase							:     str									=	rescale_factor if rescale_factor is not None else self.rescale_factor
											lowercase							:     Union[str, Any]									=	do_center_crop if do_center_crop is not None else self.do_center_crop
											lowercase							:     Optional[Any]									=	(
											    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
											)

											lowercase							:     Optional[int]									=	size if size is not None else self.size
											lowercase							:     List[str]									=	get_size_dict(SCREAMING_SNAKE_CASE_			,   default_to_square=SCREAMING_SNAKE_CASE_       )
											lowercase							:     List[Any]									=	crop_size if crop_size is not None else self.crop_size
											lowercase							:     str									=	get_size_dict(SCREAMING_SNAKE_CASE_			,   param_name='''crop_size'''       )

											lowercase							:     Optional[int]									=	make_list_of_images(SCREAMING_SNAKE_CASE_       )

											if not valid_images(SCREAMING_SNAKE_CASE_       ):
																	raise ValueError(
																	    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																	    '''torch.Tensor, tf.Tensor or jax.ndarray.'''       )

											if do_resize and size is None:
																	raise ValueError('''Size must be specified if do_resize is True.'''       )

											if do_rescale and rescale_factor is None:
																	raise ValueError('''Rescale factor must be specified if do_rescale is True.'''       )

											if do_center_crop and crop_size is None:
																	raise ValueError('''Crop size must be specified if do_center_crop is True.'''       )

											# All transformations expect numpy arrays.
											lowercase							:     str									=	[to_numpy_array(SCREAMING_SNAKE_CASE_       ) for image in images]

											if do_resize:
																	lowercase							:     List[Any]									=	[self.resize(image=SCREAMING_SNAKE_CASE_			,   size=SCREAMING_SNAKE_CASE_			,   resample=SCREAMING_SNAKE_CASE_       ) for image in images]

											if do_center_crop:
																	lowercase							:     Any									=	[self.center_crop(image=SCREAMING_SNAKE_CASE_			,   size=SCREAMING_SNAKE_CASE_       ) for image in images]

											if do_rescale:
																	lowercase							:     int									=	[self.rescale(image=SCREAMING_SNAKE_CASE_			,   scale=SCREAMING_SNAKE_CASE_       ) for image in images]

											# the pretrained checkpoints assume images are BGR, not RGB
											if do_flip_channel_order:
																	lowercase							:     Optional[int]									=	[self.flip_channel_order(image=SCREAMING_SNAKE_CASE_       ) for image in images]

											lowercase							:     str									=	[to_channel_dimension_format(SCREAMING_SNAKE_CASE_			,   SCREAMING_SNAKE_CASE_       ) for image in images]

											lowercase							:     Optional[int]									=	{"""pixel_values""": images}
											return BatchFeature(data=SCREAMING_SNAKE_CASE_			,   tensor_type=SCREAMING_SNAKE_CASE_       )




					def 							_lowerCAmelCase						(      self   :       Union[str, Any]			,   lowerCAmelCase   :       Optional[Any]			,   lowerCAmelCase   :       Union[str, Any] = None       ):
											lowercase							:     Dict									=	outputs.logits

											# Resize logits and compute semantic segmentation maps
											if target_sizes is not None:
																	if len(SCREAMING_SNAKE_CASE_       ) != len(SCREAMING_SNAKE_CASE_       ):
																							raise ValueError(
																							    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''       )

																	if is_torch_tensor(SCREAMING_SNAKE_CASE_       ):
																							lowercase							:     Any									=	target_sizes.numpy()

																	lowercase							:     str									=	[]

																	for idx in range(len(SCREAMING_SNAKE_CASE_       )       ):
																							lowercase							:     str									=	torch.nn.functional.interpolate(
																							    logits[idx].unsqueeze(dim=0       )			,   size=target_sizes[idx]			,   mode='''bilinear'''			,   align_corners=SCREAMING_SNAKE_CASE_       )
																							lowercase							:     int									=	resized_logits[0].argmax(dim=0       )
																							semantic_segmentation.append(SCREAMING_SNAKE_CASE_       )
											else:
																	lowercase							:     List[Any]									=	logits.argmax(dim=1       )
																	lowercase							:     Any									=	[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

											return semantic_segmentation







import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


a      :  Optional[int]			     =			logging.getLogger(__name__)







@dataclass
class 				_a				(   _lowerCAmelCase					):
			A      		=	field(
			    default=0.0 ,		metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to SortishSamler or not.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''whether to use adafactor'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default='''linear''' ,		metadata={'''help''': F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()					)}'''} ,		)






